Analytic applications typically use abstractions, shortened records, or other aggregate forms of transactional business data. Business data may include key figure data, which may include data that can be aggregated using an appropriate function. For example, sales totals for a particular time period can be summed or a minimum (or maximum) daily sales total can be calculated. In another example, pricing information can be extracted from larger, transactional data structures and then stored in a more efficient aggregate table for reporting and analysis. In some instances, new key figure data can be derived from existing key figure data by, for example, a calculation (e.g., calculating a margin based on a purchase process) or a restriction (e.g., determine open sales given a restriction of sales data to sales orders with a status of “in progress”). Semantically related key figures may also be grouped, thus forming a key figure structure.
Moreover, key figure data may be grouped by one or more items of information, in which case the grouping can be referred to as characteristic data. For example, sales totals can be grouped by sales person, sales region, product, etc., and aggregated sales totals can be calculated for each characteristic grouping. Analytic applications may include key figure data and characteristic data, both of which may be used during reporting and analysis of the underlying data.